Three-Dimensional Anatomy of the Hypoglossal Canal: A Plastinated Histologic Study.

OBJECTIVE: To provide a precise description of the morphology and morphometry of the hypoglossal canal (HC) and its relationship with surrounding structures by using the epoxy sheet plastination technique. METHODS: Thirty human cadaveric heads were plastinated into 5 sets of gross transparent plastination slices and 43 sets of ultrathin plastination sections. The HC were examined at both macro- and micro levels in these plastination sections and the reconstructed 3-dimensional visualization model. RESULTS: The HC was an upward arched bony canal with a dumbbell-shaped lumen. According to the arched trajectory of its bottom wall, the HC could be divided into a medial ascending segment and a lateral descending segment. The thickness of the compact bone in the middle part of the HC was thinner than that at the intracranial and extracranial orifices. In 14 of 43 sides (32.6%), the posterior wall or the roof of the HC were disturbed by passing venous channels which communicated the posterior condylar emissary vein and the inferior petroclival vein. The trajectory of hypoglossal nerve in HC is mainly from anterosuperior to posteroinferior. The meningeal dura and the arachnoid extended into the HC along the hypoglossal nerve to form the dural and arachnoid sleeves and then fused with the nerve near the extracranial orifice of the HC. CONCLUSIONS: Knowledge of the detailed anatomy of the HC can be helpful in avoiding surgical complications when performing surgery for lesions and the occipital condylar screw placement in this complex area.

The medial wall and medial compartment of the cavernous sinus: an anatomic study using plastinated histological sections.

The medial wall of the cavernous sinus (CS) has a significant role in evaluation and treatment of pituitary adenomas. This study was conducted to clarify the fine architecture of the medial wall and medial compartment of the CS at both macro- and micro-levels in twenty-one human cadaveric heads by using the epoxy sheet plastination technique. The sellar part medial wall is an intact dural layer that separates the CS from the pituitary gland. This dural wall adhered to the diaphragma sellae and the periosteum of the sella turcica to form fibrous triangles. Eight micro-protrusions of the pituitary gland were found at both sides of that wall. The thickness of the sellar part medial wall at its central portion was significantly thinner than that at the other surrounding portions. From the superior view, tortuous intracavernous carotid arteries can be divided into outward bending type and inward bending type. The inward bending intracavernous carotid was apt to bent towards the central part of the sellar part medial wall, where there were usually wide and short fibrous bands with more densely stained connective tissues between them. The micro-protrusion of the pituitary gland in the medial wall of the CS could provide an anatomical basis for the occult tumor invasion and the recurrence of residual tumor. Different bending facing states of tortuous intracavernous carotid arteries in the lateral direction may be a factor of the determination of the direction of growth of pituitary tumors.

CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network.

Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat-top-hat strategy. Then, grey-principal component analysis (grey-PCA) is used to transform RGB images into gray images that can preserve detailed features. After that, the local shift-invariant shearlet transform (LSIST) method decomposes the images into the low-pass (LP) and high-pass (HP) sub-bands, efficiently restoring all significant characteristics in various scales and directions. The HP sub-bands are fed to two branches of the Siamese convolutional neural network (CNN) by process of feature detection, initial segmentation, and consistency verification to effectively capture smooth edges, and textures. While the LP sub-bands are fused by employing local energy fusion using the averaging and selection mode to restore the energy information. The proposed method is validated by subjective and objective quality assessments. The subjective evaluation is conducted by a user case study in which twelve field specialists verified the superiority of the proposed method based on precise details, image contrast, noise in the fused image, and no loss of information. The supremacy of the proposed method is further justified by obtaining 0.6836 to 0.8794, 0.5234 to 0.6710, and 3.8501 to 8.7937 gain for QFAB, CRR, and AG and noise reduction from 0.3397 to 0.1209 over other methods for objective parameters.

Evaluation of the Upper Airway Morphology: The Role of Cone Beam Computed Tomography.

Cone beam computed tomography (CBCT) has several applications in dentomaxillofacial diagnosis. Frequently, the imaged volume encompasses the upper airway. This article provides a systematic approach to airway analysis and the implications of the anatomic and pathologic alterations. It discusses the role of CBCT in management of obstructive sleep apnea (OSA) patients. This paper also highlights technological advances that combine CBCT imaging with computational modeling of the airway and the potential clinical applications of such technologies.